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Bayesian Statistics at the FDA: The Pioneering Experience with Medical Devices Greg Campbell, Ph.D. Director, Division of Biostatistics Center for Devices and Radiological Health Food and Drug Administration Florida State University Dept. of Statistics 50th Anniv. April 17, 2009
Outline • What are devices? • The nature of medical devices and their regulation • Bayesian statistics in medical device trials • Adaptive trials
Center for Drug Eval. & Research Center for Biologic Eval. & Research Center for Devices & Rad. Health Food and Drug Administration Center for Food Safety & Nuitrition Center for Veterinary Medicine Nat’l Center for Toxicol. Research
What are Medical Devices? Definition by exclusion: any medical item for use in humans that is not a drug nor a biological product PRK lasers pacemakers defibrillators spinal fixation devices glucometers artificial heartshearing aids latex gloves artificial skinsoftware, etc intraocular lenses MRI machines breast implants surgical instruments thermometers (drug-coated) stents home kit for AIDS diagnostic test kits bone densitometers artificial hips
What is a Drug-Eluting Stent? Example:Cordis’ Cypher™ Sirolimus-Eluting Coronary Stent Components • Stent Platform & Delivery System • Carrier(s) • Drug
Devices Not Drugs -- The Differences • Different Alphabet SoupIDE -- Investigational Device ExemptionPMA -- PreMarket Approval510(k) -- Substantial Equivalence---not bioequivalence • A Single Confirmatory Trial (not 2). • A ‘Sham’ Control Trial may not be possible • Masking (blinding) may be impossible for patients, health care professionals, investigators • Usually don’t use Phase I, IIA, IIB, III, IV
Devices Not Drugs -- The Differences (Cont.) • Bench/Mechanical Testing not PK/PD • Mechanism of Action often well understood • Effect tends to be localized rather than systemic, physical not pharmacokinetic • Pre-clinical Animal Studies (not for toxicity) • Number & Size of Device Companies • About 15,000 registered firms • Median device company size--under 50 employees (Many are new start-up companies.) • Implants (skill dependent; learning curve)
The Nature of Medical Device Studies • Whereas drugs are discovered, devices evolve; they are constantly being “improved”; life length of a device is 1-2 years. • Rapidly changing technology
Why Did CDRH Launch the Bayesian Effort? • Devices often have a great deal of prior information. • The mechanism of action is physical (not pharmacokinetic or pharmacodynamic) and local (not systemic) • Devices usually evolve in small steps whereas drugs are discovered. • Computationally feasible due to the gigantic progress in computing hardware and algorithms • The possibility of bringing good technology to the market in a timely manner by arriving at the same decision sooner or with less current data was of great appeal to the device industry.
Early Decisions We Made • Restrict to data-based prior information. A subjective approach is fraught with danger. • Companies need access to good prior information to make it worth their risk. • FDA needs to work with the companies to reach an agreement on the validity of any prior information. • Need to bring the industry and FDA review staff up to speed • New decision-rules for clinical study success
Important Lessons Learned Early • Bayesian trials need to be prospectively designed. (It is almost never a good idea to switch from frequentist to Bayesian or vice versa.) • Companies need to meet early and often with CDRH. The prior information needs to be identified in advance as well as be agreed upon and legal. • The control group cannot be used a source of prior information for the new device, especially if the objective is to show the new device is non-inferior.
Important Lessons Learned Early (cont.) • Both the label and the Summary of Safety and Effectiveness (SS&E) of the device need to change. • A successful company generally has a solid Bayesian statistician (or someone who really wants to learn) as an employee or consultant. • The importance of simulation • Entire FDA review team plays a big role
The Importance of Simulation • We need to understand the operating characteristics of the Bayesian submissions. • Why? The Type 1 error probability (or some analog of it) protects the US public from approving products that are ineffective or unsafe. • So simulate to show that Type 1 error (or some analog of it) is well-controlled. • Simulations can also be of help in estimating the approximate size of the trial and the strategy of interim looks. Usually Bayesian studies are not a fixed size.
The Role of Education • Educational Efforts are important: HIMA/FDA Workshop “Bayesian Methods in Medical Devices Clinical Trials” in 1998. • FDA internal course “Bayesian Statistics for Medical Device Trials: What the Non-Statistician Needs to Know” in 1999 and 2001. • Lots of short courses and seminars and one-on-one consults
“Can Bayesian Approaches to Studying New Treatments Improve Regulatory Decision-Making?” • Title of a Workshop in 2004 • Jointly sponsored and planned by FDA and Johns Hopkins University • Presentations by Janet Woodcock, Bob Temple, Steve Goodman, Tom Louis, Don Berry, Greg Campbell, 3 case studies and panel discussions. • Held May 20-21, 2004, at NIH • August, 2005 issue of the journal Clinical Trials is devoted to this workshop
Legal Sources of Prior Information Based on Data • Company’s own previous studies: pilots, studies conducted overseas, very similar devices, registries • Permission legally obtained to use another company’s data • Studies published in the literature. For the above, summaries of previous studies may not be sufficient to formulate prior; e.g., patient-level data are often necessary.
Bayesian Statistics: Submissions to CDRH • At least 15 Original PMAs and PMA Supplements have been approved with a Bayesian analysis as primary. • The Supplements include stent systems, a heart valve, and spinal cage systems. • Many IDEs have also been approved. • Several applications for “substantial equivalence” (510(k)s) • A number of reviews are in process.
Areas of Bayesian Application for Medical Device Studies • Incorporation of data-based prior information into a current trial, allowing the data from the current trial to “gain strength” as dictated through one of a number of methodologies. • Prediction models for surrogate variables • Analysis of multi-center trials (e.g., use hierarchical models to address variability among centers) • Bayesian subgroup analysis • Sensitivity analysis for missing data • Flexibility of a Bayesian design and analysis in the event of an ethically sensitive device. This could be useful in adesign with a changing randomization ratio in an adaptive design (as in ECMO). An added advantage is to increase enrollment and address investigator equipoise.
Hierarchical Bayesian Modeling • Use a hierarchical model a place usually non-informative priors at the highest level of the hierarchy • For example, consider a number of past studies and teh current one, each with different numbers of patients and assume that the patients within a study are exchangeable and the studies are exchangeable among each other. • Place a (non-informative) prior to reflect the distribution of the studies. • This model borrows strength adaptively form past studies to model the current study.
Adaptive Trials • Adaptive trials require meticulous planning; it is not just an attitude of changing the trial in the middle without a lot of pre-planning. • “Adaptive by design” • You can only adapt to the changes you could have anticipated (not the ones you can’t or don’t) • Adaptive bandwagon
Familiar Types of Adaptive Trial Designs • For time-to-event studies, the number of events and not the number of patients that drives the power. • In trials with low recruitment rates, DMCs often adapt by changing the inclusion/exclusion criteria, increasing the number of sites, changes in the endpoint, other changes in the protocol, etc. • Such changes require an IDE (or IND) amendment. • Group sequential designs
Adaptive Approaches • Dose-finding in Phase II drug studies • Sample size re-estimation • Seamless Phase II-III studies • Dropping an arm in a study with 3 or more arms • Response Adaptive Treatment Allocation • Bayesian sample size • Bayesian predictive modeling
FDA Draft Guidance Document • “Draft Guidance for the Use of Bayesian Statistics in Medical Device Trials” released May, 2006 http://www.fda.gov/cdrh/osb/guidance/1601.pdf • Public meeting to comment on the draft was held in Rockville MD in July, 2006.
Adaptive Treatment Allocation • Change the randomization ratio during the course of the trial. • Two different approaches: • Balance of baseline covariates in the randomization • Response-Adaptive Treatment Allocation.
Example: ECMO • ExtraCorporeal Membrane Oxygenation (ECMO) for the treatment of persistent pulmonary hypertension of the newborn (PPHN) • Univ. Michigan trial • Randomized Play-the-Winner • One baby received conventional medical therapy (B) and then 11 ECMO (R): BRRRRRRRRRRR • Lesson: avoid extremes with very few patients in one arm • A more recent British demonstration trial (UK ECMO Group, 1996) • 1:1 randomization with sequential monitoring • 30 deaths of 93 in ECMO arm, 54 out of 94 in control arm (p=0.0005)
Decision Theory, Clinical Trials and Risk • Use Statistical Decision theory to decide when to curtail a study, when the loss of enrolling more patients is larger than that of stopping (for either success or failure). (Lewis, 1996) • Risk versus benefit (in public health terms). • For FDA this would require quantitative (non-economic) measures of benefit as well as risk. Often in premarket submissions this is a balance between safety and effectiveness. • Health outcomes researchers use QALYs (Quality Adjusted Life Years).
Recent FDA Advisory Committee Panel Meetings • One in November, 2008, that used an adaptive design with a non-informative prior and a separate rule to stop recruiting and another to stop for success or futility http://www.fda.gov/ohrms/dockets/ac/08/slides/2008-4393s1-00-Index.html • One in March, 2009, that used prior information from a previous trial in a Bayesian hierarchical model http://www.fda.gov/ohrms/dockets/ac/09/slides/2009-4419s1-00-index.html
Conclusion • Bayesian statistics can be used in a regulatory setting for medical devices. • It has application for situations with prior information as well as in adaptive trials • Statistical issues that confront medical devices are challenging and exciting.